packages <- c("here", "knitr", "tidyverse", "kableExtra", "RColorBrewer", 
              "colorspace", "adegenet", "poppr", "hierfstat", "cowplot", 
              "forcats", "geosphere", "vegan", "patchwork", "ggforce", 
              "magick", "ggrepel", "gplots", "reshape2", "sf", "polysat",
              "strataG")

installed_packages <- packages %in% rownames(installed.packages())
if(any(!installed_packages)) {
  install.packages(packages[!installed_packages])
}

lapply(packages, library, character.only = TRUE)

Microsatellite markers

The two sets of microsatellite data were exported, from the provided Excel sheets, to tab-separated values (TSV) files named 95_samples_8_loci.txt and 66_samples_21_loci.txt. These were then reconciled with each other using Python (code below) and exported to a single, merged TSV file called merged_loci.str for analysis using STRUCTURE

Only one locus in one individual (GA86, locus 13) differed between the two datasets, and this was removed. Where genotype data were available in one dataset and not the other, the one with available genotype data was retained.

Several changes to the formatting of the data were made to be compatible with STRUCTURE:

  1. Alleles were renamed to be two digits long, i.e. 113 was converted to 13.
  2. Pairs of columns representing each allele were shifted to be in consecutive rows rather than columns.
  3. Missing values were changed from “NA” to “-99”.
  4. Sites were encoded as a single digit number, 1 to 7.
#!/usr/bin/env python3

# Read the data from each of the two datasets in separately and then reconcile.

_95 = {}  # To store the data from the 95 ants/8 loci dataset.

sample_to_site = {}  # Keep track of which ants belong to which site.

# Parse the 95/8 file, line-by-line:

with open("95_samples_8_loci.txt", "r") as f:
    lines = f.readlines()

    header = lines[0].rstrip().split("\t")
    loci = [i[-2:] for i in header[2:] if not i == '']

    for l in lines[1:]:
        l = l.rstrip().split("\t")
        sample = l[0]
        site = l[1]
        sample_to_site[sample] = site

        allele1 = [i[1:] if i != "NA" else "NA" for i in l[2::2]]
        allele2 = [i[1:] if i != "NA" else "NA" for i in l[3::2]]
        alleles = list(zip(allele1, allele2))
        
        _95[sample] = {}
        
        for i in range(len(alleles)):
            locus = loci[i]
            allele = alleles[i]
        
            # Replace the NAs with -99, as per STRUCTURE's requirements.
            if allele == ("NA", "NA"):
                allele = ("-99", "-99")
        
            _95[sample][locus] = allele

_95_loci = loci[:]  # Keep a list of the 95/8 loci.

_66 = {}  # To store the data from the 66 ants/21 loci dataset.

# Parse the 66/21 file in the same way:

with open("66_samples_21_loci.txt", "r") as f:
    lines = f.readlines()
    
    header = lines[0].rstrip().split("\t")
    loci = [i[-2:] for i in header[2:] if not i == '']
    
    for l in lines[1:]:
        l = l.rstrip().split("\t")
        sample = l[0]
        
        allele1 = [i[1:] if i != "NA" else "NA" for i in l[2::2]]
        allele2 = [i[1:] if i != "NA" else "NA" for i in l[3::2]]
        alleles = list(zip(allele1, allele2))
        
        _66[sample] = {}
        
        for i in range(len(alleles)):
            locus = loci[i]
            allele = alleles[i]
            
            # Replace NAs with -99 again.
            if allele == ("NA", "NA"):
                allele = ("-99", "-99")
            
            _66[sample][locus] = allele

_66_loci = loci[:]  # A list of the 66/21 loci.

all_loci = sorted(list(set(_95_loci + _66_loci)))  # A merged list of all loci.

merged = {}  # New dictionary to stored the merged data in.

# Go through each sample in the 95/8 dataset, which has all the ants:

for sample in _95:
    for locus in all_loci:
        if locus in _95[sample]:
            # Sample is in the 66/21 dataset also:
            if sample in _66:
                # If the loci match between the datasets then add to the merged set:
                if _95[sample][locus] == _66[sample][locus]:
                    if sample in merged:
                        merged[sample][locus] = _95[sample][locus]
                    else:
                        merged[sample] = {locus: _95[sample][locus]}
                # One or other dataset has missing values for a loci:
                elif (_95[sample][locus] == ("-99", "-99")) or (_66[sample][locus] == ("-99", "-99")):
                    # If missing in 95/8 dataset, then use value from 66/21 dataset:
                    if _95[sample][locus] == ("-99", "-99"):
                        if sample in merged:
                            merged[sample][locus] = _66[sample][locus]
                        else:
                            merged[sample] = {locus: _66[sample][locus]}
                    # Otherwise, if missing from 66/21, then use 95/8 value:
                    else:
                        if sample in merged:
                            merged[sample][locus] = _95[sample][locus]
                        else:
                            merged[sample] = {locus: _95[sample][locus]}
                # If different in both datasets, then print sample and locus:
                else:
                    print(sample, locus)
            # Add ants that are in 95/8 dataset but not 66/21:
            else:
                if sample in merged:
                    merged[sample][locus] = _95[sample][locus]
                else:
                    merged[sample] = {locus: _95[sample][locus]}
        # Add loci that are in 66/21 dataset but not 95/8 dataset:
        else:
            if sample in _66:
                if sample in merged:
                    merged[sample][locus] = _66[sample][locus]
                else:
                    merged[sample] = {locus: _66[sample][locus]}

# Make a sorted list of all of the sites, and a number/site dictionary:
all_sites = sorted(list(set(sample_to_site.values())))
site_to_num = {all_sites[i]: str(i+1) for i in range(len(all_sites))}

# Write the merged data to a file that can be used as input to STRUCTURE, with
# each allele on successive lines:
with open("merged_loci.str", "w") as out:
    out.write("\t".join(all_loci) + "\n")
    for sample in merged:
        allele1s = []
        allele2s = []
        for locus in all_loci:
            if locus in merged[sample]:
                allele1s.append(merged[sample][locus][0])
                allele2s.append(merged[sample][locus][1])
            else:
                allele1s.append("-99")
                allele2s.append("-99")
        out.write("\t".join([sample, site_to_num[sample_to_site[sample]], "1"] + allele1s) + "\n")
        out.write("\t".join([sample, site_to_num[sample_to_site[sample]], "1"] + allele2s) + "\n")

The site numbers are as follows:

sites <- read_delim(here("data/metadata", "site_nums.txt"), col_names = c("number", "site", "name"), delim="\t")

sites_table <- sites
colnames(sites_table) <- c("Population number", "Site ID", "Site name")

sites_table %>%
  kbl(caption="Site numbers and names", format = "html", table.attr = "style='width:50%;'") %>%
  kable_styling(position = "left")
Site numbers and names
Population number Site ID Site name
1 AV Aviemore
2 BX Broxa Forest
3 CF Cropton Forest
4 FB Feshiebridge
5 GB Gaitbarrows
6 LS Longshaw Estate
7 AB Abernethy 
nest_locs_file <- here("data/metadata", "nest_lats_lons.tsv")
nest_lats_lons <- read_tsv(nest_locs_file)

lats_lons <- nest_lats_lons %>%
  group_by(pop) %>%
  mutate(lat=mean(lat), lon=mean(lon)) %>%
  ungroup() %>%
  select(pop, lat, lon) %>%
  distinct() %>%
  left_join(sites, c("pop" = "number")) %>%
  rename(num = pop) %>%
  select(num, site, name, lon, lat) %>%
  write_csv(here("data/metadata", "site_lats_lons.csv"))

After merging the two sets of microsatellite data, the poppr R library was used to filter out any loci or individuals that had more than 25% missing data, so as not to bias the analysis with sparse sample data, producing a final input file merged_filtered.str. This was done as follows:

## Read in microsatellite data and convert to GenInd format object

df <- read_tsv(here("data/genotypes", "merged_loci_r_haploid_only.str"), col_names = TRUE)
pop <- df$pop
ind_names <- df$ind
df <- df %>% dplyr::select(-pop, -ind)
rownames(df) <- ind_names
ant_gen <- df2genind(df, ploidy=1, sep="", pop=pop)

## Filter data, to exclude missing loci and genotypes

ant_gen = missingno(ant_gen, type = "loci", cutoff = 0.25)
ant_gen = missingno(ant_gen, type = "geno", cutoff = 0.25)

## Remove duplicates

mlg <- mlg(ant_gen)

dups_ant = mlg.id(ant_gen)
ant_dups <- c()
for (i in dups_ant){
  if (length(dups_ant[i]) > 1){
    print(i)
    ant_dups <- c(ant_dups, i[1:length(i)-1])
  }
}

ant_nodups = indNames(ant_gen)[! indNames(ant_gen) %in% ant_dups]
ant_gen = ant_gen[ant_nodups, ]

## Write filtered data to file

genind2genalex(ant_gen, filename=here("data/metadata", "merged_filtered.str"), sep="\t", overwrite=TRUE)

Figure 1: Allelic richness and private alleles.

## Make data frame with population information for the individuals

pops <- data.frame(id = rownames(ant_gen$tab), pop = ant_gen$pop)
pops$pop <- as.numeric(as.character(pops$pop))

## Merge in site data

sites <- read_delim(here("data/metadata", "site_nums.txt"), col_names = c("number", "site", "name"), delim="\t")
sites <- pops %>%
  left_join(sites, b=c(pop = "number"))

ant_ids <- rownames(ant_gen$tab)

rownames(sites) <- sites$id
sites <- sites[ant_ids,]
sites_copy = sites

## Calculate allelic richness values and merge in site data

allelic_richness <- allelic.richness(ant_gen)
allelic_richness_wide <- data.frame(allelic_richness$Ar)

sites_unique <- sites %>%
  select(pop, site) %>%
  distinct()

rownames(sites_unique) <- sites_unique$pop

col_numbers <- str_extract(colnames(allelic_richness_wide), "\\d+")
col_numbers <- as.character(col_numbers)
name_mapping <- setNames(sites_unique$site, as.character(sites_unique$pop))
colnames(allelic_richness_wide) <- name_mapping[col_numbers]

allelic_richness_long <- allelic_richness_wide %>%
  rownames_to_column(var = "locus") %>%
  pivot_longer(cols = -locus, names_to = "site", values_to = "ar")

## Plot allelic richness values

p1 <- allelic_richness_long %>%
  ggplot(aes(x = fct_reorder(site, ar, .fun = mean, .desc = TRUE), y = ar)) +
  geom_boxplot() +
  labs(
    x = "Site",
    y = "Allelic richness"
  ) +
  theme_minimal(base_size = 10) +
  theme(
    plot.title = element_text(size = 12, hjust = 0.5),
    legend.position = "none",
    axis.title.x = element_text(vjust = -0.5),
    axis.title.y = element_text(vjust = 0.5),
    plot.margin = margin(5, 5, 2.5, 5, unit = "mm")
  )

## Calculate private alleles

pa <- private_alleles(ant_gen, report="data.frame", level="population")
pa$population <- as.numeric(pa$population)

pa <- pa %>%
  dplyr::mutate(count=ifelse(count>0, 1, 0)) %>%
  dplyr::group_by(population) %>%
  dplyr::summarise(pa_count=sum(count)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(population) %>%
  left_join(sites_unique, by=c("population" = "pop")) %>%
  dplyr::select(site, pa_count) %>%
  dplyr::arrange(-pa_count)

ord <- pa$site
pa$site <- factor(pa$site, levels=ord)

## Plot private alleles

p2 <- ggplot(data=pa, aes(x=site, y=pa_count)) + 
  geom_col() +
  theme_minimal(base_size=10) +
    theme(plot.title=element_text(size=12, hjust=0.5), 
          legend.position="none", 
          axis.title.x = element_text(vjust=-0.5), 
          axis.title.y = element_text(vjust=0.5),
          plot.margin = margin(5, 5, 2.5, 5, unit = "mm")) +
  xlab("Site") +
  ylab("Number of private alleles")

## Merge allelic richness and private allele plots and save

p <- plot_grid(p1, p2, ncol = 2, labels = c("A", "B"), label_size = 12)
ggsave(here("figures", "figure_1_allelic_richness_private_alleles.tiff"), dpi=800, height=70, width=165, units="mm", bg="white", plot=p)
ggsave(here("figures", "figure_1_allelic_richness_private_alleles.png"), dpi=800, height=70, width=165, units="mm", bg="white", plot=p)
p
**Fig. 1**: Allelic richness and private alleles.

Fig. 1: Allelic richness and private alleles.

Figure 2: Allele frequencies

## Calculate allele frequencies for all sites

allele_freqs <- rraf(ant_gen)
allele_freqs <- unlist(allele_freqs, recursive=TRUE)
allele_freqs <- data.frame(freq = allele_freqs)

## Plot allele frequencies for all sites

p1 <- ggplot(data=allele_freqs, aes(x=freq)) + 
  geom_histogram(binwidth = 0.1) +
  theme_minimal(base_size=10) +
    theme(plot.title=element_text(size=10, hjust=0.5), 
          legend.position="none", 
          axis.title.x = element_text(vjust=-0.5), 
          axis.title.y = element_text(vjust=0.5),
          plot.margin = margin(5, 5, 2.5, 5, unit = "mm")) +
  xlab("Allele frequency") +
  ylab("Number of alleles") +
  xlim(0, 1) + ylim(0, 15) +
  ggtitle("All locations")

## Calculate allele frequencies for Feshiebridge

ant_gen_fb <- ant_gen[ant_gen@pop == 4]
allele_freqs_fb <- rraf(ant_gen_fb)
allele_freqs_fb <- unlist(allele_freqs_fb, recursive=TRUE)
allele_freqs_fb <- data.frame(freq = allele_freqs_fb)

## Plot allele frequencies for Feshiebridge

p2 <- ggplot(data=allele_freqs_fb, aes(x=freq)) + 
  geom_histogram(binwidth = 0.1) +
  theme_minimal(base_size=10) +
    theme(plot.title=element_text(size=10, hjust=0.5), 
          legend.position="none", 
          axis.title.x = element_text(vjust=-0.5), 
          axis.title.y = element_text(vjust=0.5),
          plot.margin = margin(5, 5, 2.5, 5, unit = "mm")) +
  xlab("Allele frequency") +
  ylab("Number of alleles") +
  xlim(0, 1) + ylim(0, 30) +
  ggtitle("Feshiebridge")

## Merge plots and save

p <- plot_grid(p1, p2, ncol = 2, labels = c("A", "B"), label_size = 12)
ggsave(here("figures", "figure_2_allele_frequencies.tiff"), dpi=800, height=70, width=165, units="mm", bg="white", plot=p)
ggsave(here("figures", "figure_2_allele_frequencies.png"), dpi=800, height=70, width=165, units="mm", bg="white", plot=p)
p
**Fig. 2**: Allele frequencies.

Fig. 2: Allele frequencies.

M-ratio tests to detect population bottlenecks

The code below runs an M-ratio test with a function from the strataG library by stratum (population), and then calculates the mean M-ratio across the loci for each population. Apparently, a rule of thumb from Garza & Williamson (2001) is that an M-ratio of < ~0.68 can indicate a recent bottleneck. The values below are all above that, so this is further evidence of no recent bottlenecks.

## Read in genotype data and refomat as gtypes

df <- read_tsv(here("data/genotypes", "merged_loci_r_haploid_only.str"), col_names = TRUE)
df[, 3:23] <- lapply(df[, 3:23], function(x) as.numeric(x) + 100)

g <- df2gtypes(
  x          = df,
  ploidy     = 1,
  id.col     = 1,
  strata.col = 2,
  loc.col    = 3
)

## Run M-ratio test

mr <- mRatio(g, by.strata=TRUE, rpt.size = 8:1)

## Calculate means of M-ratios

sites_ids_names <- sites %>%
  select(pop, site, name) %>%
  distinct()

mr_means <- mr %>%
  group_by(stratum) %>%
  summarize(
    mean_mratio = mean(m.ratio, na.rm = TRUE)
  ) %>%
  rename(pop = stratum) %>%
  mutate(pop=as.numeric(pop)) %>%
  left_join(sites_ids_names, by=c("pop" = "pop")) %>%
  select(site, name, mean_mratio) %>%
  arrange(site)

colnames(mr_means) <- c("Site ID", "Site", "Mean M-ratio")

mr_means %>%
  write.csv(here("tables", "m_ratio_test_results.csv"), row.names = FALSE)

mr_means %>%
  kbl(caption="M-ratio test results", format = "html", table.attr = "style='width:50%;'") %>%
  kable_styling(position = "left")
M-ratio test results
Site ID Site Mean M-ratio
AB Abernethy 0.9365079
AV Aviemore 0.9007937
BX Broxa Forest 0.8484127
CF Cropton Forest 0.8682540
FB Feshiebridge 0.9563492
GB Gaitbarrows 0.9833333
LS Longshaw Estate 0.8674603

Figure 3: PCA and DAPC of microsatellite data

## Run PCA

all_sites <- sites_unique$site

x = tab(ant_gen, NA.method = "mean")
pca1 = dudi.pca(x, scannf = FALSE, scale = FALSE, nf = 3)
percent = pca1$eig/sum(pca1$eig)*100

ind_coords = as.data.frame(pca1$li)
colnames(ind_coords) = c("Axis1","Axis2","Axis3")
ind_coords$Ind = indNames(ant_gen)
ind_coords$Site = as.numeric(as.character(ant_gen$pop))

## Find centroids of sites

ind_coords <- left_join(ind_coords, sites_unique, by=c("Site" = "pop"))
centroid = aggregate(cbind(Axis1, Axis2, Axis3) ~ site, data = ind_coords, FUN = mean)
ind_coords = left_join(ind_coords, centroid, by = "site", suffix = c("",".cen"))

## Merge in nest and hence ant host data

id_to_nest = read_tsv(here("data/metadata", "id_to_nest.txt"), col_names = c("id", "nest"))
host_species = read_tsv(here("data/metadata", "host_species.txt"), col_names = c("nest", "host"))

ind_coords <- left_join(ind_coords, id_to_nest, by=c("Ind" = "id"))
ind_coords <- left_join(ind_coords, host_species, by=c("nest" = "nest"))
ind_coords$site <- factor(ind_coords$site, levels = all_sites)

## Plot PCA

cols = brewer.pal(nPop(ant_gen), "Set2")
xlab = paste("Axis 1 (", format(round(percent[1], 1), nsmall=1)," %)", sep="")
ylab = paste("Axis 2 (", format(round(percent[2], 1), nsmall=1)," %)", sep="")

p1 <- ggplot(data = ind_coords, aes(x = Axis1, y = Axis2)) +
  geom_hline(yintercept = 0) +
  geom_vline(xintercept = 0) +
  geom_segment(aes(xend = Axis1.cen, yend = Axis2.cen, colour = site), show.legend = FALSE) +
  geom_point(aes(colour = site, fill = site, shape = factor(host)), size = 2, show.legend = TRUE) +
  geom_label(data = centroid, aes(label = site, fill = site), size = 3, show.legend = FALSE) +
  scale_fill_manual(values = cols) +
  scale_colour_manual(values = cols) +
  labs(x = xlab, y = ylab) +
  theme_minimal(base_size=10) +
  theme(plot.title=element_text(size=10, hjust=0.5), 
        plot.margin = margin(5, 5, 5, 5, unit = "mm"),
        legend.position="none",
        axis.title.x = element_text(vjust=-0.5), 
        axis.title.y = element_text(vjust=0.5)) + 
  guides(fill = FALSE, color = FALSE, shape=guide_legend(title="Host species"))  +
  ggtitle("PCA")

## Run DAPC

set.seed(123)
x = tab(ant_gen, NA.method = "mean")
crossval = xvalDapc(x, ant_gen$pop, result = "groupMean", xval.plot = FALSE)

crossval$`Root Mean Squared Error by Number of PCs of PCA`
crossval$`Number of PCs Achieving Highest Mean Success`
crossval$`Number of PCs Achieving Lowest MSE`
numPCs = as.numeric(crossval$`Number of PCs Achieving Lowest MSE`)

dapc1 = dapc(ant_gen, ant_gen$pop, n.pca = numPCs, n.da = 3)
percent = dapc1$eig/sum(dapc1$eig)*100

ind_coords = as.data.frame(dapc1$ind.coord)
colnames(ind_coords) = c("Axis1","Axis2","Axis3")
ind_coords$Ind = indNames(ant_gen)
ind_coords$Site = as.numeric(as.character(ant_gen$pop))

## Merge in site data

ind_coords <- left_join(ind_coords, sites_unique, by=c("Site" = "pop"))

## Filter out Gaitbarrows due to small sample size

ind_coords = ind_coords %>%
  filter(site != "GB")

## Find centroids of sites

centroid = aggregate(cbind(Axis1, Axis2, Axis3) ~ site, data = ind_coords, FUN = mean)
ind_coords = left_join(ind_coords, centroid, by = "site", suffix = c("",".cen"))

ind_coords <- left_join(ind_coords, id_to_nest, by=c("Ind" = "id"))
ind_coords <- left_join(ind_coords, host_species, by=c("nest" = "nest"))

ind_coords$site <- factor(ind_coords$site, levels = all_sites)

## Plot PCA

cols = brewer.pal(nPop(ant_gen), "Set2")
xlab = paste("Axis 1 (", format(round(percent[1], 1), nsmall=1)," %)", sep="")
ylab = paste("Axis 2 (", format(round(percent[2], 1), nsmall=1)," %)", sep="")

p2 <- ggplot(data = ind_coords, aes(x = Axis1, y = Axis2)) +
  geom_hline(yintercept = 0) +
  geom_vline(xintercept = 0) +
  geom_segment(aes(xend = Axis1.cen, yend = Axis2.cen, colour = site), show.legend = FALSE) +
  geom_point(aes(colour = site, fill = site, shape = factor(host)), size = 2, show.legend = TRUE) +
  geom_label(data = centroid, aes(label = site, fill = site), size = 3, show.legend = FALSE) +
  scale_fill_manual(values = cols) +
  scale_colour_manual(values = cols) +
  labs(x = xlab, y = ylab) +
  theme_minimal(base_size=10) +
  theme(plot.title=element_text(size=10, hjust=0.5), 
        plot.margin = margin(5, 5, 2.5, 5, unit = "mm"),
        legend.position="bottom",
        legend.box.margin = margin(l = -30),
        legend.title = element_blank(),
        legend.text = element_text(size = 10),
        axis.title.x = element_text(vjust=-0.5), 
        axis.title.y = element_text(vjust=0.5)) + 
  guides(fill = FALSE, color = FALSE, shape = guide_legend(override.aes = list(size = 3))) +
  ggtitle("DAPC")

## Merge plots and save

p <- plot_grid(p1, p2, ncol = 1, labels = c("A", "B"), label_size = 12, rel_heights = c(1, 1.15))
ggsave(here("figures", "figure_3_PCA_DAPC.tiff"), dpi=800, height=165, width=82.5, units="mm", bg="white", plot=p)
ggsave(here("figures", "figure_3_PCA_DAPC.png"), dpi=800, height=165, width=82.5, units="mm", bg="white", plot=p)
p
**Fig. 3**: PCA and DAPC of microsatellite data.

Fig. 3: PCA and DAPC of microsatellite data.

Figure 4: Pairwise linearised Fst vs. distance scatter plots

A Python script—split_genotypes_by_site.py—was used to split the combined genotypes file merged_loci_r_haploid_only.str into separate site-specific genotype (.str extension) and nest number metadata files:

#!/usr/bin/env python3
"""
Process nest IDs and generate site-specific structure and nest numbering files.
"""

# Map IDs to nests from file.
id_to_nest = {}
with open("id_to_nest.txt", "r") as f:
    for line in f:
        parts = line.rstrip().split()
        id_to_nest[parts[0]] = parts[1]

# Get unique sites (first two characters of nest) and nests.
sites = list({v[:2] for v in id_to_nest.values()})
nests = list(set(id_to_nest.values()))

# Assign sequential numbers to nests per site.
nest_nums = {}
for site in sites:
    nest_nums[site] = {}
    nest_num = 1
    for nest in sorted(nests, key=lambda x: int(x[2:])):
        if nest.startswith(site):
            nest_nums[site][nest] = str(nest_num)
            nest_num += 1

# Process the main structure file and output site-specific files.
nests_done = []
for i in range(1, 8):
    with open("merged_loci_r_haploid_only.str", "r") as f:
        lines = f.readlines()
        with open(f"site_{i}_hap.str", "w") as site_out, \
             open(f"nest_nums_{i}.txt", "w") as nest_out:
            site_out.write(lines[0])
            for line in lines[1:]:
                if line.split()[1] == str(i):
                    parts = line.rstrip().split()
                    nest = id_to_nest[parts[0]]
                    site = nest[:2]
                    nest_num = nest_nums[site][nest]
                    if nest not in nests_done:
                        nest_out.write("\t".join([nest_num, nest]) + "\n")
                    nests_done.append(nest)
                    site_out.write("\t".join([parts[0], nest_num] + parts[2:]) + "\n")

The main genoptypes object and the separate site-specific ones were used to create scatter plots of pairwise linearised Fst versus distance at the site level, and nest level (excluding Cropton Forest).

## Between-site linearised Fst vs. distance

ant_fst = genet.dist(ant_gen, method = "WC84", diploid=FALSE) %>% round(digits = 3)
fst_mat = as.matrix(ant_fst)

labels = sites_unique[rownames(fst_mat),]$site
rownames(fst_mat) <- labels
colnames(fst_mat) <- labels

## Linearise Fst

linear_fst_mat = fst_mat / (1 - fst_mat)

## Between-site distance matrix

lats_lons = read_csv(here("data/metadata", "site_lats_lons.csv"), col_names=TRUE)
dist_mat <- distm(lats_lons[4:5], fun = distGeo) / 1000

dist_mat = as.matrix(dist_mat)

labels = lats_lons$site
rownames(dist_mat) <- labels
colnames(dist_mat) <- labels

dist_mat <- dist_mat[rownames(linear_fst_mat), rownames(linear_fst_mat)]

## Run Mantel test

fst_dist <- mantel(linear_fst_mat, dist_mat, method = "spearman", permutations = 9999, na.rm = TRUE)
all_mantel <- data.frame(site=c("all"), r_statistic=c(fst_dist$statistic), p_value=c(fst_dist$signif), n=length(rownames(ant_gen$tab)))

## Merge matrices

linear_fst_long <- as.data.frame(as.table(linear_fst_mat)) %>%
  rename(site1 = Var1, site2 = Var2, linear_fst = Freq)

dist_long <- as.data.frame(as.table(dist_mat)) %>%
  rename(site1 = Var1, site2 = Var2, dist = Freq)

merged_df <- linear_fst_long %>%
  inner_join(dist_long, by = c("site1", "site2")) %>%
  mutate(
    # Ensure consistent ordering of pairs
    site1_ordered = pmin(as.character(site1), as.character(site2)), 
    site2_ordered = pmax(as.character(site1), as.character(site2)), 
    pair = paste(site1_ordered, site2_ordered, sep = "_")
  ) %>%
  select(pair, linear_fst, dist) %>%
  distinct(pair, .keep_all = TRUE) %>%  # Remove duplicate flipped pairs
  filter(!grepl("^([A-Za-z]+)_\\1$", pair))  # Remove self-comparisons (AV_AV, FB_FB)

## Scatter plot of pairwise linear Fst vs. distances

p1 <- ggplot(data = merged_df, aes(y = linear_fst, x = dist)) +
  geom_point() +
  theme_minimal(base_size=10) +
  theme(plot.title=element_text(size=10, hjust=0.5), 
        legend.position="none", 
        axis.title.x = element_text(vjust=-0.5), 
        axis.title.y = element_text(vjust=0.5),
        plot.margin = margin(5, 5, 2.5, 5, unit = "mm")) +
  labs(
    y = "Linearised Fst (Fst / 1 - Fst)",
    x = "Distance (km)"
  ) +
  xlim(0, 600) + ylim(0, 1)

## Per-site linearised Fst vs. distance

nest_locs_file <- here("data/metadata", "nest_lats_lons.tsv")
id_to_nest_file = here("data/metadata", "id_to_nest.txt")

make_merged_fst_dist_df <- function(nest_locs_file, id_to_nest_file, genotypes_file, nests_file, population_number, population_id){
  ## Read in nest locations and ant ID to nest mapping
  nest_lats_lons <- read_tsv(nest_locs_file)
  id_to_nest = read_tsv(id_to_nest_file, col_names = c("id", "nest"))
  
  ids_lats_lons <- id_to_nest %>%
    left_join(nest_lats_lons, by=c("nest" = "nest"))
  
  rownames(ids_lats_lons) <- ids_lats_lons$id
  
  ## Build linearised Fst matrix
  df <- read_tsv(genotypes_file, col_names = TRUE)
  pop <- df$pop
  ind_names <- df$ind
  df <- df %>% dplyr::select(-pop, -ind)
  rownames(df) <- ind_names
  ant_gen <- df2genind(df, ploidy=1, sep="", pop=pop)
  
  ant_gen = missingno(ant_gen, type = "loci", cutoff = 0.5)
  ant_gen = missingno(ant_gen, type = "geno", cutoff = 0.5)
  
  ant_fst = genet.dist(ant_gen, method = "WC84", diploid=FALSE) %>% round(digits = 3)
  fst_mat = as.matrix(ant_fst)
  fst_mat[fst_mat < 0 | is.na(fst_mat)] <- 0
  
  nest_nums <- read_tsv(nests_file, col_names=c("num", "id"))
  indices <- rownames(fst_mat)
  rownames(fst_mat) <- nest_nums[indices,]$id
  colnames(fst_mat) <- nest_nums[indices,]$id
  
  linear_fst_mat = fst_mat / (1 - fst_mat)

  ## Build distance matrix  
  lats_lons = ids_lats_lons %>%
    select(pop, nest, lon, lat) %>%
    filter(pop == population_number) %>%
    select(-pop) %>%
    distinct()
  dist_mat <- distm(lats_lons[2:3], fun = distGeo) / 1000
  
  dist_mat = as.matrix(dist_mat)
  
  labels = lats_lons$nest
  rownames(dist_mat) <- labels
  colnames(dist_mat) <- labels
  
  dist_mat <- dist_mat[rownames(linear_fst_mat), rownames(linear_fst_mat)]
  
  ## Run Mantel test between linearised Fst and distance
  fst_dist <- mantel(linear_fst_mat, dist_mat, method = "spearman", permutations = 9999, na.rm = TRUE)
  mantel_results <- c(population_id, fst_dist$statistic, fst_dist$signif, length(rownames(ant_gen$tab)))

  ## Join everything up
  linear_fst_long <- as.data.frame(as.table(linear_fst_mat)) %>%
    rename(nest1 = Var1, nest2 = Var2, linear_fst = Freq)
  
  dist_long <- as.data.frame(as.table(dist_mat)) %>%
    rename(nest1 = Var1, nest2 = Var2, dist = Freq)
  
  site_df <- linear_fst_long %>%
    inner_join(dist_long, by = c("nest1", "nest2")) %>%
    mutate(
      # Ensure consistent ordering of pairs
      nest1_ordered = pmin(as.character(nest1), as.character(nest2)),
      nest2_ordered = pmax(as.character(nest1), as.character(nest2)),
      pair = paste(nest1_ordered, nest2_ordered, sep = "_")
    ) %>%
    select(pair, linear_fst, dist) %>%
    distinct(pair, .keep_all = TRUE) %>%  # Remove duplicate flipped pairs
    filter(!grepl("^([A-Za-z0-9]+)_\\1$", pair))  # Remove self-comparisons
  
  site_df$site <- rep(population_number, dim(site_df)[1])
  
  return(list(df=site_df, mantel_results=mantel_results))
}

## Site 4: Feshiebridge

genotypes_file <- here("data/genotypes", "site_4_hap.str")
nests_file <- here("data/metadata", "nest_nums_4.txt")
population_number <- 4
population_id <- "FB"

feshiebridge <- make_merged_fst_dist_df(nest_locs_file, id_to_nest_file, genotypes_file, nests_file, population_number, population_id)
feshiebridge_df <- feshiebridge$df
feshiebridge_mantel <- feshiebridge$mantel_results

## Site 6: Longshaw

genotypes_file <- here("data/genotypes", "site_6_hap.str")
nests_file <- here("data/metadata", "nest_nums_6.txt")
population_number <- 6
population_id <- "LS"

longshaw <- make_merged_fst_dist_df(nest_locs_file, id_to_nest_file, genotypes_file, nests_file, population_number, population_id)
longshaw_df <- longshaw$df
longshaw_mantel <- longshaw$mantel_results

## Site 7: Abernethy

genotypes_file <- here("data/genotypes", "site_7_hap.str")
nests_file <- here("data/metadata", "nest_nums_7.txt")
population_number <- 7
population_id <- "AB"

abernethy <- make_merged_fst_dist_df(nest_locs_file, id_to_nest_file, genotypes_file, nests_file, population_number, population_id)
abernethy_df <- abernethy$df
abernethy_mantel <- abernethy$mantel_results

merged_df <- rbind(feshiebridge_df, longshaw_df, abernethy_df) %>%
  left_join(sites_unique, by = c("site" = "pop"))

## Combined scatter plot of site-wise linear Fst vs. distances

p2 <- ggplot(data = merged_df, aes(y = linear_fst, x = dist, shape = factor(site.y))) +
  geom_point() +
  theme_minimal(base_size=10) +
  theme(plot.title=element_text(size=10, hjust=0.5), 
        legend.position="right", 
        axis.title.x = element_text(vjust=-0.5), 
        axis.title.y = element_text(vjust=0.5),
        plot.margin = margin(5, 5, 2.5, 5, unit = "mm")) +
  labs(
    y = "Linearised Fst (Fst / 1 - Fst)",
    x = "Distance (km)"
  )  +
  scale_shape_discrete(name = "Site") +
  xlim(0, 1.5) + ylim(0, 1)

## Merge plots and save

p <- plot_grid(p1, p2, ncol = 2, labels = c("A", "B"), label_size = 12, rel_widths = c(1, 1.25))
ggsave(here("figures", "figure_4_distance_Fst_scatter_plots.tiff"), dpi=800, height=80, width=165, units="mm", bg="white", plot=p)
ggsave(here("figures", "figure_4_distance_Fst_scatter_plots.png"), dpi=800, height=80, width=165, units="mm", bg="white", plot=p)
p
**Fig. 4**: Pairwise linearised Fst vs. distance scatter plots.

Fig. 4: Pairwise linearised Fst vs. distance scatter plots. 

## Table of all Mantel test results

mantel_results <- rbind(all_mantel, feshiebridge_mantel, longshaw_mantel, abernethy_mantel)

colnames(mantel_results) <- c("Site ID", "R statistic", "P value", "n")
rownames(mantel_results) <- NULL

mantel_results %>%
  write.csv(here("tables", "mantel_test_results.csv"), row.names = FALSE)

mantel_results %>%
  kbl(caption="Mantel test results", format = "html", table.attr = "style='width:75%;'") %>%
  kable_styling(position = "left")
Mantel test results
Site ID R statistic P value n
all 0.458441558441558 0.0128968253968254 77
FB -0.315723654587031 0.856746031746032 21
LS 0.307392840070685 0.183333333333333 8
AB -0.622799155329218 0.875 8

Table 3: AMOVA results

## Read in microsatellite data again and filter

df <- read_tsv(here("data/genotypes", "merged_loci_r_haploid_only.str"), col_names = TRUE)
df[, 3:23] <- lapply(df[, 3:23], function(x) as.numeric(x) + 100)
pop <- df$pop
ind_names <- df$ind
df <- df %>% dplyr::select(-pop, -ind)
rownames(df) <- ind_names
ant_gen <- df2genind(df, ploidy=1, sep="", pop=pop)

ant_gen = missingno(ant_gen, type = "loci", cutoff = 0.25)
ant_gen = missingno(ant_gen, type = "geno", cutoff = 0.25)

mlg <- mlg(ant_gen)

dups_ant = mlg.id(ant_gen)
ant_dups <- c()
for (i in dups_ant){
  if (length(dups_ant[i]) > 1){
    print(i)
    ant_dups <- c(ant_dups, i[1:length(i)-1])
  }
}

ant_nodups = indNames(ant_gen)[! indNames(ant_gen) %in% ant_dups]
ant_gen = ant_gen[ant_nodups, ]

## Merge in nest IDs and create nest ID vector

ant_ids <- rownames(ant_gen$tab)
id_to_nest <- read_tsv(here("data/metadata", "id_to_nest.txt"), col_names = c("id", "nest"))
nests <- id_to_nest %>%
  filter(id %in% ant_ids)
rownames(nests) <- nests$id
nests <- nests[ant_ids,]
nest_vec <- nests$nest

## Merge in site IDs and create site ID vector

pops <- data.frame(id = rownames(ant_gen$tab), pop = ant_gen$pop)
pops$pop <- as.numeric(as.character(pops$pop))
sites <- read_delim(here("data/metadata", "site_nums.txt"), col_names = c("number", "site", "name"), delim="\t")
sites <- pops %>%
  left_join(sites, b=c(pop = "number"))
rownames(sites) <- sites$id
sites <- sites[ant_ids,]
sites_copy = sites
site_vec = sites$site

## Add these to genInd object and then set as strata

ant_gen@other$site <- site_vec
ant_gen@other$nest <- nest_vec
strata_df <- data.frame(other(ant_gen))
strata(ant_gen) <- strata_df

## Run the AMOVA

amova_result <- poppr.amova(ant_gen, hier = ~site/nest, nperm=999)

amova_df <- data.frame("Source of variation"=c(),
                       "Df"=c(),
                       "Sum Sq"=c(),
                       "Mean Sq"=c(),
                       "Variance component"=c(),
                       "Total variance"=c(),
                       "p-value"=c())

## Significance testing of AMOVA results

set.seed(1999)
amova_signif   <- randtest(amova_result, nrepet = 999)
plot(amova_signif)

## Table of AMOVA results

sources <- c("Between sites",
             "Between nests within sites",
             "Within host nests")

amova_df <- cbind(sources,
                  data.frame(amova_result$results)[1:3,],
                  data.frame(amova_result$componentsofcovariance)[1:3,],
                  amova_signif$pvalue)

colnames(amova_df) <- c("Source of variation",
                        "Df",
                        "Sum Sq",
                        "Mean Sq",
                        "Variance component",
                        "Total variance",
                        "p-value")

rownames(amova_df) <- NULL

amova_df %>%
  write.csv(here("tables", "table_3_amova_results.csv"), row.names = FALSE)

amova_df %>%
  kbl(caption="<strong>Table 3:</strong> AMOVA results", format = "html", table.attr = "style='width:100%;'", escape = FALSE) %>%
  kable_styling(position = "left")
Table 3: AMOVA results
Source of variation Df Sum Sq Mean Sq Variance component Total variance p-value
Between sites 6 59.79281 9.965469 0.7026586 22.746209 0.001
Between nests within sites 24 59.04952 2.460397 0.0519778 1.682608 0.485
Within host nests 46 107.38642 2.334487 2.3344875 75.571183 0.001

Mitochondrial haplotypes

Figure 5: Haplotype map and haplotype network

To visualize relationships between mtDNA haplotypes, a haplotype network was constructed using our new data and the eight additional mtDNA sequences available in the BOLD database (two from Finland, two from Switzerland, three from Spain and one from Norway). Sequences were aligned using MUSCLE (version 5.2.osxarm64, and the haplotype network was constructed using POPART (version 1.7) with the TCS network type.

## Read and process the haplotype data

haplotypes <- read_table(here("data/haplotypes", "haplotypes.tsv"), col_names = TRUE) %>%
  mutate(site = str_extract(nest, "\\b[A-Z]{2}")) %>%
  left_join(., sites_unique, by = c("site" = "site")) %>%
  group_by(site) %>%
  summarise(across(starts_with("HAP"), sum)) %>%
  ungroup()

## Read and join latitude and longitude information

## Note that the approx_lats_lons have been nudged to allow the pie charts to
## be placed on the map figure better and these are not geographically accurate!

approx_lats_lons <- read_csv(here("data/metadata", "approx_lats_lons.csv"), col_names = TRUE) %>%
  left_join(., sites_unique, by = c("num" = "pop")) %>%
  select(site, lon, lat)

haplotypes <- haplotypes %>%
  left_join(approx_lats_lons, by = "site") %>%
  mutate(rad = 0.35) %>%            # radius for pie placement
  mutate(n = rowSums(across(starts_with("HAP"))))

## Define the color palette for the haplotypes

n <- 6
cols <- brewer.pal(n, "Set2")
names(cols) <- sprintf("HAP%1d", 1:n)

## Create a nested data frame for the pie charts with computed angles.
## Note: The computed pie chart data use the haplotype coordinates as given.

pie.list <- haplotypes %>%
  pivot_longer(cols = starts_with("HAP"), names_to = "type", values_to = "value") %>%
  group_by(site, lon, lat, rad) %>%
  mutate(total = sum(value),
         prop = value / total,
         end = 2 * pi * cumsum(prop),
         start = lag(end, default = 0)) %>%
  ungroup() %>%
  group_by(site, lon, lat, rad) %>%
  nest(data = c(type, value, prop, start, end)) %>%
  mutate(
    pie.grob = map(data, ~ {
      p_temp <- ggplot(.) +
        geom_arc_bar(aes(x0 = 0, y0 = 0, r0 = 0, r = 1,
                         start = start, end = end, fill = type),
                     color = "black", size = 0.3) +
        scale_fill_manual(values = cols) +
        coord_fixed() +
        theme_void() +
        theme(legend.position = "none")
      ggplotGrob(p_temp)
    }),
    subgrob = pmap(list(pie.grob, rad, lon, lat),
                   function(pg, r, lon, lat) {
                       annotation_custom(grob = pg,
                                           xmin = lat - r, xmax = lat + r,
                                           ymin = lon - r, ymax = lon + r)
                   }
  )) %>%
  ungroup()

# min_x <- min(haplotypes$lat) - margin
# max_x <- max(haplotypes$lat) + margin
# min_y <- min(haplotypes$lon) - margin
# max_y <- max(haplotypes$lon) + margin

min_x = -7
max_x = 2
min_y = 53
max_y = 58

## Get world map data (world map is standard: x = long, y = lat)

world <- map_data("world", resolution = 0)

## Build the base map

map <- ggplot(data = world, aes(x = long,
                                y = lat,
                                group = group)) +
  geom_polygon(fill = "darkseagreen", color = "black", size=0.35) +
  coord_quickmap(xlim = c(min_x, max_x), ylim = c(min_y, max_y)) +
  xlab("Longitude") +
  ylab("Latitude") +
  theme(panel.background = element_rect(fill = "lightsteelblue2"),
        panel.grid.minor = element_blank(),
        panel.grid.major = element_line(colour = "grey90", size = 0.5),
        axis.title.x = element_text(vjust=-0.5, size=10), 
        axis.title.y = element_text(vjust=0.5, size=10))

## Add a tiny tile layer so that the haplotype legend appears

map <- map +
  geom_tile(data = haplotypes %>%
              pivot_longer(cols = starts_with("HAP"), names_to = "type", values_to = "value"),
            aes(x = lat,
                y = lon,
                fill = type),
            color = "black", width = 0.01, height = 0.01, inherit.aes = FALSE) +
  scale_fill_manual(values = cols, name = "Haplotype")

## Overlay each pie chart grob on the map using the annotations

for (sg in pie.list$subgrob) {
  map <- map + sg
}

## Add labels for total counts per site

p_temp <- map + 
  theme(
    legend.position = "none",
    plot.margin = margin(5, 5, 2.5, 5, unit = "mm")
  )

ggsave(here("figures", "haplotype_map_no_labels.tiff"), dpi=800, height=80, width=80, units="mm", bg="white", plot=p_temp)
ggsave(here("figures", "haplotype_map_no_labels.png"), dpi=800, height=80, width=80, units="mm", bg="white", plot=p_temp)

map <- map +
  geom_label_repel(data = haplotypes,
                   aes(label = paste("N=", n, sep = ""),
                       x = lat,
                       y = lon),
                   nudge_y = 0, inherit.aes = FALSE, size = 2.75,
                   min.segment.length = 5, force = 0.75)

p1 <- map + 
  theme(
    legend.position = c(0.815, 0.715),
    legend.background = element_rect(fill = alpha("white", 1.0), color = "black", size=0.35),
    legend.key.size = unit(0.3, "cm"),
    legend.text = element_text(size = 8),
    legend.title = element_text(size = 8),
    plot.margin = margin(5, 5, 2.5, 5, unit = "mm")
  )

## Read in SVG of haplotype network

p2 <- ggdraw() +
  draw_image(here("data/haplotypes", "haplotype-network-reformatted.svg"), x = 0, y = 0, width = 1, height = 1) +
  theme(plot.margin = margin(5, 5, 2.5, 5, unit = "mm"))

## Merge the plots and save

p <- plot_grid(p1, p2, ncol = 2, labels = c("A", "B"), label_size = 12, rel_widths = c(1, 1))
ggsave(here("figures", "figure_5_haplotype_map_network.tiff"), dpi=800, height=80, width=165, units="mm", bg="white", plot=p)
ggsave(here("figures", "figure_5_haplotype_map_network.png"), dpi=800, height=80, width=165, units="mm", bg="white", plot=p)
p
**Fig. 5**: Haplotype map and haplotype network.

Fig. 5: Haplotype map and haplotype network. 

haplotypes <- haplotypes %>%
  left_join(., sites[3:4], by=c("site" = "site")) %>%
  select(name, starts_with("HAP")) %>%
  distinct() %>%
  rename(Site = name)

haplotypes %>%
  write.csv(here("tables", "table_4_mitochondrial_haplotypes.csv"), row.names=FALSE)

haplotypes %>%
  kbl(caption="<strong>Table 4:</strong> Mitochondrial haplotypes", format = "html", table.attr = "style='width:50%;'", escape = FALSE) %>%
  kable_styling(position = "left")
Table 4: Mitochondrial haplotypes
Site HAP1 HAP2 HAP3 HAP4 HAP5 HAP6
Abernethy 12 0 1 0 0 0
Aviemore 5 0 6 0 0 0
Broxa Forest 8 0 0 0 0 0
Cropton Forest 6 0 0 2 1 0
Feshiebridge 21 0 0 0 0 1
Gaitbarrows 0 3 0 0 0 0
Longshaw Estate 6 6 0 0 0 0

Supplementary figures

Figure S1: Between-site Fst and distance matrices

## General functions for matrices

## Get lower triangle of a correlation matrix
get_lower_tri<-function(cormat){
  cormat[upper.tri(cormat)] <- NA
  return(cormat)
}

## Get upper triangle of a correlation matrix
get_upper_tri <- function(cormat){
  cormat[lower.tri(cormat)]<- NA
  return(cormat)
}

## Reorder the matrix with hierarchical clustering
reorder_cormat <- function(cormat){
  dd <- as.dist(cormat)
  hc <- hclust(dd)
  cormat <-cormat[hc$order, hc$order]
  return(cormat)
}

## Calculate all sites Fst matrix

ant_fst = genet.dist(ant_gen, method = "WC84", diploid=FALSE) %>% round(digits = 3)
fst_mat = as.matrix(ant_fst)

labels = sites_unique[rownames(fst_mat),]$site
rownames(fst_mat) <- labels
colnames(fst_mat) <- labels
  
## Melt the correlation matrix

upper_tri <- get_upper_tri(reorder_cormat(fst_mat))
melted_cormat <- melt(upper_tri, na.rm = TRUE)

## Plot heatmap

p1 <- ggplot(data = melted_cormat, aes(Var2, Var1, fill = value))+
  geom_tile(color = "white")+
  scale_fill_gradient2(low = "#8D5B9B", high = "#fff4f1", mid = "#f786b6", 
                       midpoint = median(melted_cormat$value), 
                       limit = c(min(melted_cormat$value), max(melted_cormat$value)), 
                       space = "Lab", 
                       name=expression(F[ST])) +
  theme_minimal(base_size=10)+ 
  theme(axis.text.x = element_text(size = 8)) +
  theme(axis.text.y = element_text(size = 8)) +
  theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
  theme(plot.margin = margin(0, 2.5, 0, 2.5, unit = "mm")) +
  coord_fixed()

p1 <- p1 + geom_text(aes(Var2, Var1, label = value), color = "black", size = 2)

## Calculate all sites distance matrix

lats_lons = read_csv(here("data/metadata", "site_lats_lons.csv"), col_names=TRUE)
dist_mat <- distm(lats_lons[4:5], fun = distGeo) / 1000

dist_mat = as.matrix(dist_mat)

labels = lats_lons$site
rownames(dist_mat) <- labels
colnames(dist_mat) <- labels

dist_mat <- dist_mat[rownames(upper_tri), rownames(upper_tri)]

## Melt the distance matrix

upper_tri <- get_upper_tri(dist_mat)
melted_distmat <- melt(upper_tri, na.rm = TRUE)

## Plot heatmap

p2 <- ggplot(data = melted_distmat, aes(Var2, Var1, fill = value))+
  geom_tile(color = "white")+
  scale_fill_gradient2(low = "#8D5B9B", high = "#fff4f1", mid = "#f786b6", 
                       midpoint = max(melted_distmat$value)/2, 
                       limit = c(min(melted_distmat$value), max(melted_distmat$value)),
                       space = "Lab", 
                       name="Dist. (km)") +
  theme_minimal(base_size=10)+ 
  theme(axis.text.x = element_text(size = 8)) +
  theme(axis.text.y = element_text(size = 8)) +
  theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
  theme(plot.margin = margin(0, 2.5, 0, 2.5, unit = "mm")) +
  coord_fixed()

p2 <- p2 + geom_text(aes(Var2, Var1, label = round(value)), color = "black", size = 2)

## Merge heatmaps and save

p <- plot_grid(p1, p2, ncol = 2, labels = c("A", "B"), label_size = 12, rel_widths = c(1, 1.04))
ggsave(here("figures", "figure_S1_sites_Fst_distance_matrix.tiff"), dpi=800, height=80, width=165, units="mm", bg="white", plot=p)
ggsave(here("figures", "figure_S1_sites_Fst_distance_matrix.png"), dpi=800, height=80, width=165, units="mm", bg="white", plot=p)
p
**Fig. S1**: Between-site Fst and distance matrices.

Fig. S1: Between-site Fst and distance matrices.

Figure S2: Per-site Fst and distance matrices

## Take a genotypes file and nest data, returns the heatmap
build_fst_plot <- function(genotypes_file, nests_file, min_val, max_val){
  ## Read in the genotype data
  df <- read_tsv(genotypes_file, col_names = TRUE)
  pop <- df$pop
  ind_names <- df$ind
  df <- df %>% dplyr::select(-pop, -ind)
  rownames(df) <- ind_names
  ant_gen <- df2genind(df, ploidy=1, sep="", pop=pop)
  
  ## Filter data
  ant_gen = missingno(ant_gen, type = "loci", cutoff = 0.5)
  ant_gen = missingno(ant_gen, type = "geno", cutoff = 0.5)
  
  ## Calculate the Fst matrix
  ant_fst = genet.dist(ant_gen, method = "WC84", diploid=FALSE) %>% round(digits = 3)
  fst_mat = as.matrix(ant_fst)
  fst_mat[fst_mat < 0 | is.na(fst_mat)] <- 0
  
  ## Read in the nest data, merge with Fst matrix
  nest_nums <- read_tsv(nests_file, col_names=c("num", "id"))
  rownames(nest_nums) <- nest_nums$num
  labels <- as.character(rownames(fst_mat))
  rownames(fst_mat) <- nest_nums[labels,]$id
  colnames(fst_mat) <- nest_nums[labels,]$id
  
  ## Melt the correlation matrix
  upper_tri <- get_upper_tri(reorder_cormat(fst_mat))
  row_order <- rownames(upper_tri)
  melted_cormat <- melt(upper_tri, na.rm = TRUE)
  
  ## Build the heatmap
  hm <- ggplot(data = melted_cormat, aes(Var2, Var1, fill = value))+
    geom_tile(color = "white")+
    scale_fill_gradient2(low = "#8D5B9B", high = "#fff4f1", mid = "#f786b6", 
                         midpoint = min_val + ((max_val - min_val) / 2), 
                         limit = c(min_val, max_val), 
                         space = "Lab", 
                         name=expression(F[ST])) +
    theme_minimal(base_size=6)+ 
    theme(axis.text.x = element_text(size = 6)) +
    theme(axis.text.y = element_text(size = 6)) +
    theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
    theme(plot.margin = margin(0, 2.5, 0, 2.5, unit = "mm")) +
    coord_fixed()
  
  hm <- hm + geom_text(aes(Var2, Var1, label = round(value, 3)), color = "black", size = 2)
  return(list(heatmap=hm, row_order=row_order))
}

## Takes a nest locations file, set of nests, and a population, returns a heatmap
build_dist_plot <- function(nest_locs_file, nests_file, population_number, row_order, min_val, max_val){
  ## Read in the nest numbers and locations
  nest_nums <- read_tsv(nests_file, col_names=c("num", "id"))
  nest_lats_lons <- read_tsv(nest_locs_file, col_names=TRUE)
  
  ## Filter to nests in this population
  lats_lons <- nest_lats_lons %>%
    filter(pop == population_number & nest %in% nest_nums$id)
  
  ## Calculate the distance matrix
  dist_mat <- distm(lats_lons[4:3], fun = distGeo)
  dist_mat = as.matrix(dist_mat)
  
  labels = lats_lons$nest
  rownames(dist_mat) <- labels
  colnames(dist_mat) <- labels
  
  ## Reorder and convert from metres to km
  dist_mat <- dist_mat[row_order, row_order] / 1000
  
  ## Melt the correlation matrix
  upper_tri <- get_upper_tri(dist_mat)
  melted_cormat <- melt(upper_tri, na.rm = TRUE)
  
  ## Build the heatmap
  hm <- ggplot(data = melted_cormat, aes(Var2, Var1, fill = value))+
    geom_tile(color = "white")+
    scale_fill_gradient2(low = "#8D5B9B", high = "#fff4f1", mid = "#f786b6", 
                         midpoint = min_val + ((max_val - min_val) / 2), 
                         limit = c(min_val, max_val), 
                         space = "Lab", 
                         name="Dist. (km)") +
    theme_minimal(base_size=6)+ 
    theme(axis.text.x = element_text(size = 6)) +
    theme(axis.text.y = element_text(size = 6)) +
    theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
    theme(plot.margin = margin(0, 2.5, 0, 2.5, unit = "mm")) +
    coord_fixed()
  
  hm <- hm + geom_text(aes(Var2, Var1, label = round(value, 3)), color = "black", size = 2)
  return(hm)
}

## Define variables used for all heatmaps

nest_locs_file <- here("data/metadata", "nest_lats_lons.tsv")

min_fst <- 0
max_fst <- 0.5

min_dist <- 0
max_dist <- 2.5

## Site 1: Aviemore

genotypes_file <- here("data/genotypes", "site_1_hap.str")
nests_file <- here("data/metadata", "nest_nums_1.txt")

fst <- build_fst_plot(genotypes_file, nests_file, min_fst, max_fst)
fst_hm <- fst$heatmap
row_order <- fst$row_order

dist_hm <- build_dist_plot(nest_locs_file, nests_file, 1, row_order, min_dist, max_dist)

aviemore <- plot_grid(fst_hm, dist_hm, ncol = 2, labels = c("A", "B"), label_size = 12, rel_widths = c(1, 1.04))

## Site 2: Broxa

genotypes_file <- here("data/genotypes", "site_2_hap.str")
nests_file <- here("data", "metadata/nest_nums_2.txt")

fst <- build_fst_plot(genotypes_file, nests_file, min_fst, max_fst)
fst_hm <- fst$heatmap
row_order <- fst$row_order

dist_hm <- build_dist_plot(nest_locs_file, nests_file, 2, row_order, min_dist, max_dist)

broxa <- plot_grid(fst_hm, dist_hm, ncol = 2, labels = c("C", "D"), label_size = 12, rel_widths = c(1, 1.04))

## Site 3: Cropton Forest

genotypes_file <- here("data/genotypes", "site_3_hap.str")
nests_file <- here("data/metadata", "nest_nums_3.txt")

fst <- build_fst_plot(genotypes_file, nests_file, min_fst, max_fst)
fst_hm <- fst$heatmap
row_order <- fst$row_order

dist_hm <- build_dist_plot(nest_locs_file, nests_file, 3, row_order, min_dist, max_dist)

cropton_forest <- plot_grid(fst_hm, dist_hm, ncol = 2, labels = c("E", "F"), label_size = 12, rel_widths = c(1, 1.04))

## Site 4: Feshiebridge

genotypes_file <- here("data/genotypes", "site_4_hap.str")
nests_file <- here("data/metadata", "nest_nums_4.txt")

fst <- build_fst_plot(genotypes_file, nests_file, min_fst, max_fst)
fst_hm <- fst$heatmap
row_order <- fst$row_order

dist_hm <- build_dist_plot(nest_locs_file, nests_file, 4, row_order, min_dist, max_dist)

feshiebridge <- plot_grid(fst_hm, dist_hm, ncol = 2, labels = c("G", "H"), label_size = 12, rel_widths = c(1, 1.04))

## Site 6: Longshaw

genotypes_file <- here("data/genotypes", "site_6_hap.str")
nests_file <- here("data/metadata", "nest_nums_6.txt")

fst <- build_fst_plot(genotypes_file, nests_file, min_fst, max_fst)
fst_hm <- fst$heatmap
row_order <- fst$row_order

dist_hm <- build_dist_plot(nest_locs_file, nests_file, 6, row_order, min_dist, max_dist)

longshaw <- plot_grid(fst_hm, dist_hm, ncol = 2, labels = c("I", "J"), label_size = 12, rel_widths = c(1, 1.04))

## Site 7: Abernethy

genotypes_file <- here("data/genotypes", "site_7_hap.str")
nests_file <- here("data/metadata", "nest_nums_7.txt")

fst <- build_fst_plot(genotypes_file, nests_file, min_fst, max_fst)
fst_hm <- fst$heatmap
row_order <- fst$row_order

dist_hm <- build_dist_plot(nest_locs_file, nests_file, 7, row_order, min_dist, max_dist)

abernethy <- plot_grid(fst_hm, dist_hm, ncol = 2, labels = c("K", "L"), label_size = 12, rel_widths = c(1, 1.04))

## Merge all the Fst/distance heatmaps together and save

p <- plot_grid(aviemore,
               broxa,
               cropton_forest,
               feshiebridge,
               longshaw,
               abernethy,
               ncol = 1) +
  theme(plot.margin = margin(2.5, 2.5, 2.5, 2.5, unit = "mm"))
ggsave(here("figures", "figure_S2_nests_Fst_distance_matrix.tiff"), dpi=800, height=80*6, width=165, units="mm", bg="white", plot=p)
ggsave(here("figures", "figure_S2_nests_Fst_distance_matrix.png"), dpi=800, height=80*6, width=165, units="mm", bg="white", plot=p)
p
**Fig. S2**: Per-site Fst and distance matrices.

Fig. S2: Per-site Fst and distance matrices.

Figure S3: Location maps for the sites

## Define variables used for all maps

margin <- 0.002
nest_locs_file <- here("data/metadata", "nest_lats_lons.tsv")

## Takes a nest locations file and population number, returns a nest location map
make_site_map <- function(nest_locs_file, population_number){
  ## Read in nest locations data
  lats_lons = read_tsv(nest_locs_file)
  
  ## Filter for population
  lats_lons <- lats_lons %>%
    filter(pop == population_number)
  
  ## Set map margins
  min_x = min(lats_lons$lon) - margin
  max_x = max(lats_lons$lon) + margin
  min_y = min(lats_lons$lat) - margin
  max_y = max(lats_lons$lat) + margin
  
  ## Plot map
  world = map_data("world", resolution=0)
  
  map <- ggplot(data = world, aes(x = long,
                                  y = lat)) +
    geom_polygon(fill = "darkseagreen") +
    coord_quickmap(xlim = c(min_x, max_x), ylim = c(min_y, max_y)) +
    xlab("Longitude") +
    ylab("Latitude") +
    theme(panel.background = element_rect(fill = "lightsteelblue2"),
          panel.grid.minor = element_blank(),
          panel.grid.major = element_line(colour = "grey90", size = 0.5),
          axis.title.x = element_text(vjust=-0.5, size=8), 
          axis.title.y = element_text(vjust=0.5, size=8),
          axis.text.x = element_text(size=7),
          axis.text.y = element_text(size=7)) +
    scale_x_continuous(labels = function(x) sprintf("%.4g", x))
  
  ## Add nest labels
  map <- map + geom_point(data=lats_lons, aes(x=lon, y=lat)) +
    geom_label_repel(data=lats_lons, aes(x=lon, y=lat, label=nest), size=2.5, min.segment.length=0)
  
  return(map)
}

## Site 1: Aviemore

aviemore_map <- make_site_map(nest_locs_file, 1)

## Site 2:

broxa_map <- make_site_map(nest_locs_file, 2)

## Site 3:

cropton_forest_map <- make_site_map(nest_locs_file, 3)

## Site 4:

feshiebridge_map <- make_site_map(nest_locs_file, 4)

## Site 6:

longshaw_map <- make_site_map(nest_locs_file, 6)

## Site 7:

abernethy_map <- make_site_map(nest_locs_file, 7)

## Merge all the maps and save

p <- plot_grid(aviemore_map,
               broxa_map,
               cropton_forest_map,
               feshiebridge_map,
               longshaw_map,
               abernethy_map,
               ncol = 2,
               labels = c("A", "B", "C", "D", "E", "F"),
               label_size = 12) +
  theme(plot.margin = margin(2.5, 2.5, 2.5, 2.5, unit = "mm"))
ggsave(here("figures", "figure_S3_nest_location_maps.tiff"), dpi=800, height=80*3, width=165, units="mm", bg="white", plot=p)
ggsave(here("figures", "figure_S3_nest_location_maps.png"), dpi=800, height=80*3, width=165, units="mm", bg="white", plot=p)
p
**Fig. S3**: Location maps for the sites.

Fig. S3: Location maps for the sites.

Figure S4: STRUCTURE ancestry plots

Population structure analysis was performed using STRUCTURE with MAXPOPS=1, BURNIN=10000, NUMREPS=20000, and LOCPRIOR=1 (mainparams and extraparams files used are included in the GitHub repository) and values of K from 1 to 10. The Bash script below was used to test the range of values of K with a specific number of replicate runs.

#!/bin/bash

# structure binary and mainparams and extraparams files should be in
# the same directory as this script

# Function to display usage information
usage() {
    echo "Usage: $0 -i <input_file> -o <output_directory> -k <value_k> -r <value_r>"
    exit 1
}

# Parse named parameters using getopts
while getopts ":i:o:k:r:" opt; do
    case "${opt}" in
        i)
            input_file=${OPTARG}
            ;;
        o)
            output_dir=${OPTARG}
            ;;
        k)
            value_k=${OPTARG}
            ;;
        r)
            value_r=${OPTARG}
            ;;
        *)
            usage
            ;;
    esac
done
shift $((OPTIND - 1))

# Validate that all parameters were provided
if [ -z "${input_file}" ] || [ -z "${output_dir}" ] || [ -z "${value_k}" ] || [ -z "${value_r}" ]; then
    usage
fi

# Create the output directory if it doesn't exist
mkdir -p "${output_dir}"

for (( k = 1; k <= value_k; k++ ))
do
    for (( r = 1; r <= value_r; r++ ))
    do
        ./structure -i "${input_file}" -o "${output_dir}/k${k}_${r}" -K "${k}" -m mainparams -e extraparams
    done
done

StructureSelector was used to estimate the optimal value/s of K from which to estimate the populations’ ancestry, using Evanno’s delta K method. For each value of K, 5 replicate STRUCTURE analyses were run, and the resulting ancestry proportions were averaged across each value of K using the CluMPAK online server.

## Read in sites and STRUCTURE/CluMPAK results for K=2

sites <- read_delim(here("data/metadata", "site_nums.txt"), col_names = c("number", "site", "name"), delim="\t")
ancestry <- read.table(here("data/structure", "CluMPAK_K2.tsv"), header=FALSE)
ancestry <- ancestry[,c(4, 6:7)]
colnames(ancestry) <- c("pop", "C1", "C2")
ancestry$id <- indNames(ant_gen)

## Set up colours

n <- 2
colrs <- brewer.pal.info[brewer.pal.info$colorblind == TRUE, ]
cols = brewer.pal(nPop(ant_gen), "Accent")
cols = cols[1:n]
names(cols) <- sprintf("C%01d", 1:length(cols))

## Pivot data

ancestry <- ancestry %>%
  pivot_longer(
    cols = c(C1, C2),
    names_to = "cluster",
    values_to = "ancestry"
  )

## Merge in site data

ancestry <- ancestry %>%
  left_join(sites, by=c("pop" = "number")) %>%
  dplyr::select(-name)

ancestry$site <- factor(ancestry$site, levels=c("AV", "FB", "AB", "LS", "CF", "BX", "GB"))

## Plot

p1 <- ggplot(ancestry, aes(fill=factor(cluster), y=ancestry, x=id)) +
  geom_bar(position="stack", stat="identity", alpha=1.0, width=0.8) +
  facet_grid(~site, scales="free_x", space = "free_x") +
  theme_minimal(base_size=10) +
  theme(axis.text.x = element_text(size=4, angle = 90, vjust = 0.55, margin=unit(c(-0.20,0,0,0), "cm")),
        axis.title.x = element_text(margin=unit(c(0.25,0,0,0), "cm")),
        strip.text.x = element_text(size=6, margin=margin(b=0, t=0)),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  theme(panel.spacing.x = unit(0.6, "lines"),
        plot.margin = margin(5, 5, 5, 5, unit = "mm")) +
  scale_fill_manual(values=cols, name="Cluster") +
  xlab("ID") + ylab("Proportion of ancestry")

## Read in sites and STRUCTURE/CluMPAK results for K=5

ancestry <- read.table(here("data/structure", "CluMPAK_K5.tsv"), header=FALSE)
ancestry <- ancestry[,c(4, 6:10)]
colnames(ancestry) <- c("pop", "C1", "C2", "C3", "C4", "C5")
ancestry$id <- indNames(ant_gen)

## Set up colours

n <- 5
colrs <- brewer.pal.info[brewer.pal.info$colorblind == TRUE, ]
cols = brewer.pal(nPop(ant_gen), "Accent")
cols = cols[1:n]
names(cols) <- sprintf("C%01d", 1:length(cols))

## Pivot data

ancestry <- ancestry %>%
  pivot_longer(
    cols = c(C1, C2, C3, C4, C5),
    names_to = "cluster",
    values_to = "ancestry"
  )

## Merge in site data

ancestry <- ancestry %>%
  left_join(sites, by=c("pop" = "number")) %>%
  dplyr::select(-name)

ancestry$site <- factor(ancestry$site, levels=c("AV", "FB", "AB", "LS", "CF", "BX", "GB"))

## Plot the ancestries and save

p2 <- ggplot(ancestry, aes(fill=factor(cluster), y=ancestry, x=id)) +
  geom_bar(position="stack", stat="identity", alpha=1.0, width=0.8) +
  facet_grid(~site, scales="free_x", space = "free_x") +
  theme_minimal(base_size=10) +
  theme(axis.text.x = element_text(size=4, angle = 90, vjust = 0.55, margin=unit(c(-0.20,0,0,0), "cm")),
        axis.title.x = element_text(margin=unit(c(0.25,0,0,0), "cm")),
        strip.text.x = element_text(size=6, margin=margin(b=0, t=0)),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  theme(panel.spacing.x = unit(0.6, "lines"),
        plot.margin = margin(5, 5, 5, 5, unit = "mm")) +
  scale_fill_manual(values=cols, name="Cluster") +
  xlab("ID") + ylab("Proportion of ancestry")

p <- plot_grid(p1, p2, ncol = 1, labels = c("A", "B"), label_size = 12, rel_widths = c(1, 1))
ggsave(here("figures", "figure_S4_STRUCTURE_plots.tiff"), dpi=800, height=120, width=165, units="mm", bg="white", plot=p)
ggsave(here("figures", "figure_S4_STRUCTURE_plots.png"), dpi=800, height=120, width=165, units="mm", bg="white", plot=p)
p
**Fig. S4**: STRUCTURE ancestry plots.

Fig. S4: STRUCTURE ancestry plots.

Table S1: SPAGeDi gene diversity (He) values

This is a table of the gene diversity results from the SPAGeDi output calculated in the Table S2 section below.

gene_diversity <- read.table(
  here("data/spagedi", "spagedi_output.txt"), skip = 24, nrows = 8, sep = "\t", header = FALSE, fill = TRUE) %>%
  select(2,3,10)

colnames(gene_diversity) <- c("Site", "Sample size", "He")
gene_diversity$Site[gene_diversity$Site == "All categories confounded"] <- "All sites"
num_idx <- suppressWarnings(!is.na(as.numeric(gene_diversity$Site)))
gene_diversity$Site[num_idx] <- sites$name[ match(as.numeric(gene_diversity$Site[num_idx]), sites$number) ]

gene_diversity <- gene_diversity %>%
    mutate(Site = factor(Site, levels = c(sort(unique(Site[Site != "All sites"])), "All sites"))) %>%
  arrange(Site)

gene_diversity %>%
  write.csv(here("tables", "table_S1_spagedi_gene_diversity.csv"), row.names = FALSE)

gene_diversity %>%
  kbl(caption="<strong>Table S1:</strong> Gene diversity values from SPAGeDi", format = "html", table.attr = "style='width:50%;'", escape = FALSE) %>%
  kable_styling(position = "left")
Table S1: Gene diversity values from SPAGeDi
Site Sample size He
Abernethy 12 0.2144
Aviemore 12 0.2730
Broxa Forest 9 0.4405
Cropton Forest 13 0.3742
Feshiebridge 19 0.2512
Gaitbarrows 2 NA
Longshaw Estate 10 0.3915
All sites 77 0.4168

Table S2: SPAGeDi kinship values

Spatial analysis was performed using SPAGeDi 1.5d 2017 (Hardy and Vekemans 2002) to calculate the overall and site-level gene diversity corrected for sample size (He) (Nei 1978) and to estimate kinship and its relationship with spatial distribution of samples. The Loiselle kinship estimator (Loiselle et al. 1995) is used, because it is suitable for haploid data and is robust to the presence of low frequency alleles. SPAGeDi was run using the ‘pairwise’, ‘within pairs’, and ‘whole sample reference allele frequencies’ options.

## Create genambig object from ant genotype data

genambig_obj <- as.genambig(ant_gen)

n_samp <- nInd(ant_gen)
ploidy_vector <- rep(1, n_samp)
Ploidies(genambig_obj) <- ploidy_vector
PopInfo(genambig_obj) <- as.integer(pop(ant_gen))
PopNames(genambig_obj) <- levels(pop(ant_gen))
Usatnts(genambig_obj) <- rep(2, nLoc(ant_gen))

## Merge in nest location data

nest_locs_file <- here("data/metadata", "nest_lats_lons.tsv")
nest_lats_lons <- read_tsv(nest_locs_file)

ids_lats_lons <- id_to_nest %>%
  left_join(nest_lats_lons, by=c("nest" = "nest"))

rownames(ids_lats_lons) <- ids_lats_lons$id

## Convert lats/lons to metre coordinates that will work better with SPAGeDi

coords <- ids_lats_lons[Samples(genambig_obj),] %>%
  select(X=lon, Y=lat)

## 4326 is the code for the WGS84 coordinate system, which uses latitude and longitude in degrees

coords_sf <- st_as_sf(coords, coords = c("X", "Y"), crs = 4326)

## Transform to UK UTM zone and convert to metres

coords_sf_proj <- st_transform(coords_sf, crs = 27700)
coords_metres <- st_coordinates(coords_sf_proj)
  
rownames(coords_metres) <- Samples(genambig_obj)

## Write to a SPAGeDi format file

write.SPAGeDi(
  object   = genambig_obj,
  file     = here("data/spagedi", "spagedi_input.txt"),
  allelesep= "",
  digits   = 3,
  spatcoord = coords_metres
)

## Read in SPAGeDi output

## Kinship values
kinship <- read.table(here("data/spagedi", "spagedi_output.txt"), skip = 126, header = FALSE, sep = "\t")
kinship <- kinship[!apply(kinship, 1, function(row) {
  all(is.na(row) | trimws(as.character(row)) == "")
}), ]
kinship <- kinship[, colSums(is.na(kinship) | kinship == "") != nrow(kinship)]
kinship <- kinship %>%
  select(-4)
colnames(kinship) <- c("Locus",
                       "Pairwise kinship within host nests",
                       "Pairwise kinship between host nests within sites",
                       "Slope kinship against linear distance",
                       "Slope kinship against log distance")

kinship %>%
  write.csv(here("tables", "table_S2_spagedi_kinship.csv"), row.names = FALSE)

kinship %>%
  kbl(caption="<strong>Table S2:</strong> Kinship results from SPAGeDi", format = "html", table.attr = "style='width:75%;'", escape = FALSE) %>%
  kable_styling(position = "left")
Table S2: Kinship results from SPAGeDi
Locus Pairwise kinship within host nests Pairwise kinship between host nests within sites Slope kinship against linear distance Slope kinship against log distance
ALL LOCI 0.2450 0.2306 0.0000432 -0.0048644
loc13 -0.0250 0.0929 0.0000131 0.0277037
loc14 0.1801 0.1236 -0.0002640 -0.0828370
loc35 0.1617 0.2537 0.0003122 0.0091815
loc45 0.2962 0.2328 -0.0000380 -0.0349106
loc48 0.4207 0.4369 -0.0004018 -0.0476400
loc53 0.5793 0.3419 0.0016971 0.4048120
loc54 0.2401 0.1392 -0.0000575 -0.0369808
loc58 0.2471 0.3054 -0.0002473 -0.0934013

Table S3: Haplotype sequences metadata

These are the mappings of the IDs used here on the haplotype network to the associated database IDs, and various other metadata:

read_csv(here("tables", "table_S3_haplotype_sequences_metadata.csv"), col_names = TRUE)  %>%
  kbl(caption="<strong>Table S3:</strong> Haplotype sequences metadata", format = "html", table.attr = "style='width:100%;'", escape = FALSE) %>%
  kable_styling(position = "left")
Table S3: Haplotype sequences metadata
Database ID Database Our ID Country Site name Nest ID Ant ID Latitude Longitude
OQ376286 GenBank UK1 UK Aviemore AV1 GA28 57.20916 -3.810268
OQ376285 GenBank UK2 UK Gaitbarrows GB1 GA81 54.18972 2.274528
OQ376283 GenBank UK3 UK Aviemore AV1 GA27 57.20916 -3.810268
OQ376282 GenBank UK4 UK Cropton Forest CF2 GA53 54.32631 0.831360
OQ376281 GenBank UK5 UK Cropton Forest CF3 GA57 54.32668 0.831986
OQ376284 GenBank UK6 UK Feshiebridge FB7 GA21 57.12049 -3.896572
ANTEU3610-22 BOLD SPA2 Spain NA NA NA 42.34970 1.965940
ANTEU3606-22 BOLD SPA1 Spain NA NA NA 42.34970 1.965940
ANTEU3608-22 BOLD SPA3 Spain NA NA NA 42.34970 1.965940
NOFOR031-13 BOLD NOR1 Norway NA NA NA 63.40000 10.488000
FRMAA093-20 BOLD SWI1 Switzerland NA NA NA NA NA
ANTEU2636-22 BOLD SWI2 Switzerland NA NA NA 46.79520 10.242800
ACUFI1258-13 BOLD FIN2 Finland NA NA NA 59.87500 23.942000
ACUFI1314-13 BOLD FIN1 Finland NA NA NA 61.03200 24.982000

Table S4: Ant IDs and longitude/latitudes

This table that has all of the ants, their site and nest information, and locations.

## Merge ant IDs and locations with site IDs and names

ids_lats_lons %>%
  left_join(sites_ids_names, by=c("pop" = "pop")) %>%
  left_join(host_species, by=c("nest" = "nest")) %>%
  arrange(id) %>%
  select(id, pop, site, name, nest, host, lon, lat) %>%
  rename("Ant ID" = id,
         "Population number" = pop,
         "Site ID" = site,
         "Site name" = name,
         "Nest ID" = nest,
         "Host species" = host,
         "Longitude" = lon,
         "Latitude" = lat) %>%
  write_csv(here("tables", "table_S4_ant_ids_and_locs.csv"))